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[1] Hui Ming, Zhang Xingang, Zhang Meng, et al. Modeling and linearizing broad-band power amplifier based onreal and complex-valued hybrid time-delay neural network [J]. Journal of Southeast University (English Edition), 2018, 34 (2): 139-146. [doi:10.3969/j.issn.1003-7985.2018.02.001]
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Modeling and linearizing broad-band power amplifier based onreal and complex-valued hybrid time-delay neural network()
基于实复值混合时延神经网络的宽带功放的建模和线性化
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
34
Issue:
2018 2
Page:
139-146
Research Field:
Information and Communication Engineering
Publishing date:
2018-06-20

Info

Title:
Modeling and linearizing broad-band power amplifier based onreal and complex-valued hybrid time-delay neural network
基于实复值混合时延神经网络的宽带功放的建模和线性化
Author(s):
Hui Ming1, 2, Zhang Xingang2, Zhang Meng2, Yu Chao1, Zhu Xiaowei1
1 State Key Laboratory of Millimetre Waves, Southeast University, Nanjing 211189, China
2 College of Physics and Electronic Engineering, Nanyang Normal University, Nanyang 473061, China
惠明1, 2, 张新刚2, 张萌2, 余超1, 朱晓维1
1东南大学毫米波国家重点实验室, 南京 211189; 2南阳师范学院物理与电子工程学院, 南阳473061
Keywords:
power amplifier;  neural network;  linearization;  modeling
功放;  神经网络;  线性化;  建模
PACS:
TN925
DOI:
10.3969/j.issn.1003-7985.2018.02.001
Abstract:
A new real and complex-valued hybrid time-delay neural network(TDNN)is proposed for modeling and linearizing the broad-band power amplifier(BPA). The neural network includes the generalized memory effect of input signals, complex-valued input signals and the fractional order of a complex-valued input signal module, and, thus, the modeling accuracy is improved significantly. A comparative study of the normalized mean square error(NMSE)of the real and complex-valued hybrid TDNN for different spread constants, memory depths, node numbers, and order numbers is studied so as to establish an optimal TDNN as an effective baseband model, suitable for modeling strong nonlinearity of the BPA. A 51-dBm BPA with a 25-MHz bandwidth mixed test signal is used to verify the effectiveness of the proposed model. Compared with the memory polynomial(MP)model and the real-valued TDNN, the real and complex-valued hybrid TDNN is highly effective, leading to an improvement of 5 dB in the NMSE. In addition, the real and complex-valued hybrid TDNN has an improvement of 0.6 dB over the generalized MP model in the NMSE. Also, it has better numerical stability. Moreover, the proposed TDNN presents a significant improvement over the real-valued TDNN and the MP models in suppressing out-of-band spectral regrowth.
提出了一种新型的实复值混合时延神经网络, 用于建模和线性化宽带射频功放.该神经网络包含输入信号的广义记忆效应、复值输入信号和复值输入信号模值的分数阶次, 因而其建模精度显著提高.对实复值混合时延神经网络在不同扩展常数、记忆深度、神经元数和阶数时的归一化均方误差(NMSE)进行了比较研究, 以建立一个能够有效建模宽带功放强非线性的基带模型—最优时延神经网络(TDNN).采用51 dBm宽带功放和25 MHz带宽的混合测试信号用于模型的有效性验证.测试结果表明, 实复值混合时延神经网络相比记忆多项式模型和实值时延神经网络具有更高的建模精度, NMSE提高5 dB.此外, 实复值混合时延神经网络相比广义记忆多项式模型, NMSE提高0.6 dB, 并具有更好的数值稳定性.相比实值时延神经网络和记忆多项式模型, 所提出的时延神经网络能够更好地抑制带外的频谱再生.

References:

[1] Iqdour R, Jabrane Y. High power amplifier predistorter based on fuzzy wavelet neural networks for WiMAX signals [J]. Contemporary Engineering Sciences, 2017, 10:243-251. DOI:10.12988/ces.2017.611183.
[2] Chen S, Hong X, Khalaf E, et al. Adaptive B-spline neural network based nonlinear equalization for high-order QAM systems with nonlinear transmit high power amplifier [J]. Digital Signal Processing, 2015, 40:238-249. DOI:10.1016/j.dsp.2015.02.006.
[3] Narendra K S, Parthasarathy K. Identification and control of dynamical systems using neural networks [J]. IEEE Transactions on Neural Networks and Learning Systems, 1990, 1(1):4-27. DOI:10.1109/72.80202.
[4] Ibnkahla M, Sombrin J, Castanie F, et al. Neural networks for modeling nonlinear memoryless communication channels [J]. IEEE Transactions on Communications, 1997, 45(7):768-771. DOI:10.1109/26.602580.
[5] Zhang C, Yan S X, Zhang Q J, et al. Behavioral modeling of power amplifier with long term memory effects using recurrent neural networks[C]//2013 IEEE International Wireless Symposium(IWS). Beijing, China, 2013. DOI: 10.1109/IEEE-IWS.2013.6616831.
[6] Li M, He S, Li X. Complex radial basis function networks trained by QR-decomposition recursive least square algorithms applied in behavioral modeling of nonlinear power amplifiers [J]. International Journal of RF and Microwave Computer-Aided Engineering, 2009, 19(6):634-646. DOI:10.1002/mmce.20387.
[7] Rawat M, Rawat K, Ghannouchi F M. Adaptive digital predistortion of wireless power amplifiers/transmitters using dynamic real-valued focused time-delay line neural networks [J]. IEEE Transactions on Microwave Theory and Techniques, 2010, 58(1): 95-104. DOI:10.1109/tmtt.2009.2036334.
[8] Liu TJ, Hui M, Ye Y, et al. Linearizer for Doherty power amplifiers based on augmented general fractional order memory polynomial [J]. Journal of Sichuan University: Engineering Science Edition, 2014, 46(1):107-113.(in Chinese)
[9] Ibukahla M, Sombria J, Castanie F, et al. Neural networks for modeling nonlinear memoryless communication channels [J]. IEEE Transactions on Communications, 1997, 45(7):768-771. DOI:10.1109/26.602580.
[10] Ma Y L, Yamao Y, Akaiwa Y, et al. Wideband digital predistortion using spectral extrapolation of band-limited feedback signal [J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2014, 61(7):2088-2097. DOI:10.1109/tcsi.2013.2295897.
[11] Xu G M, Liu T J, Ye Y, et al. Generalized two-box cascaded nonlinear behavioral model for radio frequency power amplifiers with strong memory effects [J]. IEEE Transactions on Microwave Theory and Techniques, 2014, 62(12):2888-2899. DOI:10.1109/tmtt.2014.2365459.
[12] Hui M, Liu T J, Zhang M. Augmented radial basis function neural network predistorter for linearisation of wideband power amplifiers [J]. Electronic Letters, 2014, 50(12):877-879. DOI:10.1049/el.2014.0667.
[13] Morgan D R, Ma Z, Kim J, et al. A generalized memory polynomial model for digital predistortion of RF power amplifiers [J]. IEEE Transactions on Signal Processing, 2006, 54(10):3852-3860. DOI:10.1109/tsp.2006.879264.
[14] Cunha T M, Lavrador P M, Lima E G, et al. Rational function-based model with memory for power amplifier behavioral modeling [C]// 2011 Workshop on Integrated Nonlinear Microwave and Millimetre-Wave Circuits(INMMIC). Vienna, Austria, 2011. DOI:10.1109/inmmic. 2011.5773328.

Memo

Memo:
Biographies: Hui Ming(1983—), male, doctor; Zhu Xiaowei(corresponding author), male, professor, doctor, xwzhu@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61561052, 61701262), the Science and Technology Foundation of Henan Province(No.182102410062, 182102210114), the Science and Technology Foundation of Henan Educational Committee(No.17A510018).
Citation: Hui Ming, Zhang Xingang, Zhang Meng, et al. Modeling and linearizing broad-band power amplifier based on real and complex-valued hybrid time-delay neural network[J].Journal of Southeast University(English Edition), 2018, 34(2):139-146.DOI:10.3969/j.issn.1003-7985.2018.02.001.
Last Update: 2018-06-20